Abstract: This project aims to establish differential artery-vein analysis in optical coherence tomography angiography (OCTA), and to validate comprehensive OCTA features for automated classification of diabetic retinopathy (DR). Early detection, prompt intervention, and reliable assessment of treatment outcomes are essential to prevent irreversible visual loss from DR. It is known that DR can target arteries and veins differently. Therefore, differential artery-vein analysis can provide better performance of DR detection and classification. However, clinical OCTA instruments lack the capability of artery-vein differentiation. During this project, we propose to use quantitative feature analysis of OCT, which is concurrently captured with OCTA, to guide artery- vein differentiation in OCTA. The first aim is to establish automated artery-vein differentiation in OCTA. In coordination with our recently demonstrated blood vessel tracking technique, OCT intensity/geometry features will be used to guide artery-vein differentiation in OCTA automatically. Differential artery-vein analysis of blood vessel tortuosity (BVT), blood vessel caliber (BVC), blood vessel density (BVD), vessel perimeter index (VPI), vessel branching coefficient (VBC), vessel branching angle (VBA), branching width ratio (BWR), fovea avascular zone area (FAZ-A) and FAZ contour irregularity (FAZ-CI) will be implemented. Key success criterion of the aim 1 study is to demonstrate robust artery-vein differentiation in OCTA, and to establish OCTA features for objective detection and classification of DR. The second aim is to validate automated OCTA classification of DR. We propose to employ ensemble machine learning to integrate multiple classifiers to achieve robust OCTA classification of DR. Key success criterion of the aim 2 study is to identify OCTA features and optimal-feature- combination to detect early DR, and to establish the correlations between the OCTA features and clinical biomarkers. The third aim is to verify OCTA prediction and evaluation of DR treatment. Our preliminary OCTA study of diabetic macular edema (DME) with anti-vascular endothelial growth factor (anti-VEGF) treatment has shown that BVD can serve as a biomarker predictive of visual improvement. During this project, we plan to test differential artery-vein analysis for DME treatment evaluation. Key success criterion of the aim 3 study is to identify artery-vein features to provide robust prediction and evaluation of DME treatment outcomes. As an alternative approach, we propose a fully convolutional neural network (FCNN) for deep machine leaning based artery-vein and DR classification. Early layers in the FCNN will produce simple features, which will be convolved and filtered into deeper layers to produce complex features for artery-vein and DR classification. Further investigation of the relationship between the new features learned through the machine learning process and clinical biomarkers will allow us to optimize the design for better DR classification. Success of this project will pave the way towards using quantitative OCTA features for early DR detection, objective prediction and assessment of treatment outcomes.